Maximizing the efficiency and speed of solving customers’ problems isn’t an easy task.

Prioritizing which multi-channel service needs the most focus – social media, individualized emails, video chatting – can feel like a guessing game. How should you allocate resources to these various avenues? How do you scale expenditures as your business grows?

The balancing act

The essence of delivering support in a growing environment is a constant tug of war between three things: ticket volume, customer satisfaction, and the resources available to execute service. The standard approach to maximizing Customer Satisfaction Scores (CSAT) with limited resources has historically been to maximize process efficiency by:

making appropriate decisions around channel (ie. maximizing channels that can handle the most volume such as chat support)

implementing deflection (ie. creating alternative customer service channels to deflect tickets from the telephone center to more cost-effective channels)

maximizing response efficiency through the use of macros (ie. templates and shortcuts that help reduce response time)

To get on the path to process efficiency, consider the following steps:

Step 1: Set up a Support Platform

Make channel decisions that focus on balancing efficiency with customer needs. Assess CSAT and synchronicity (benefits/drawbacks) of chat vs. phone vs. email support and understand the cost of each service channel. Also, it’s important to not overlook social media support requests as a recent Gartner study found that companies who ignore support requests on social media see an average churn rate that’s 15% higher than companies who don’t.

Step 2: Create a Triage Process

Just like a doctor in a hospital, triage allows a customer service team to handle urgent/high value customers first. Ideally, all tickets would be answered ASAP, but with limited resources, customer experience is an important consideration. The reverse is also true as it’s important to assess the cost of customer experience for certain customers who may not receive priority.

Efficient ticket distribution is key to segmenting urgent ticket types to address high-risk situations and service customers first who are most likely to churn. Such segmentation methodology can benefit both customer service agent satisfaction, as well as CSAT.

Yet, the work of successfully implementing a segmentation strategy is in creation, proper selection, and agent training -- an undertaking that can be resource-intensive. There are costs to implementing these processes. The key to successful execution is to use these segmentation methods when the risk of being wrong is low in an effort to minimize the impact on CSAT. Common limitations of automated segmentation include:

Implementing a keyword categorization systems that is resource-intensive to maintain.

Using manual triage that is expensive, and diverts resources from customer-facing roles.

Step 3: Use Skills-Based Routing

While it’s ideal to have all agents trained at 100% for all ticket types, representatives’ unique personalities and experiences might make them particularly effective under certain circumstances. By leveraging inherent agency strengths and maximizing training resources, certain agents will handle more complex, specialized tickets.

In addition, tickets have natural segmentation, even within priority or skill levels. When similar tickets are grouped together, there is less context-switching, a phenomenon that can slow down agents’ capacity. Categorizing increases throughput, consistency, and can positively influence the customer experience.

It’s important to note that call deflection cannot result in call avoidance, but setting up alternative means of getting answers is critical as communication evolves to include a variety of ways to reach support.

Step 5: Use Macros

Capturing an organization’s institutional knowledge and distilling it into bite-sized templates and tools gives agents the information they need to best solve issues that support customer experience. It’s key to not only have the macros available, but to make it easy for agents to access and apply that information, noting their customer service capacity. The work of successfully implementing this segmentation strategy is in creation, proper selection, and agent training -- an undertaking that can be resource-intensive.

Step 6: Leverage Machine Learning

Until now, the solutions above were best practices in the customer service world. Today, you can extend the efficiency of these approaches and help mitigate some of the tradeoffs inherent in the implementation of these approaches with a form of Artificial Intelligence (AI) called machine learning

Machine learning can help decrease the cost (and improve the efficacy) of these approaches to scaling customer support. Machine learning becomes the cutting of the Gordian tradeoff knot -- a metaphor that reminds us that disentangling an "impossible" knot is solved easily by a loophole or "thinking outside the box".

Machine learning makes the traditional tug-of-war less severe, and allows companies to effectively balance these three things in growing teams without having to sacrifice customer satisfaction, suffer a drop in ticket volume, or experience concerns around resources, namely customer service staff headcount.

Machine learning also helps optimize a balance between these three considerations by maximizing process efficiency. The predictive analysis of machine learning can watch, interpret and react appropriately, in real time – and give you a better sense of what aspect of your increasingly multichannel service environment you should prioritize. Machine learning helps focus on mastering the most critical, cost-effective and valuable channels, and then routing customers in that direction.

This progressive technology is more than a diagnostic tool, but rather a solution for customer service scaling issues. By using predictive analysis to route tickets appropriately, triage, deflect, and leverage macos, it’s possible to construct the right message to the right person at the right time. Learning who the ideal agent might be (if needed at all) to deliver the right customer response; and what channel it should be given through, along with how long it should take to deliver, are critical questions that machine learning technology can answer.

For more information about how customer service automation can help your company make the right scaling decisions, and impact the bottom line, download our Essential Guide to Automating Customer Service.